Part I. Introduction:
1. The basic framework: potential outcomes, stability, and the assignment mechanism
2. A brief history of the potential-outcome approach to causal inference
3. A taxonomy of assignment mechanisms
Part II. Classical Randomized Experiments:
4. A taxonomy of classical randomized experiments
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Part I. Introduction:
1. The basic framework: potential outcomes, stability, and the assignment mechanism
2. A brief history of the potential-outcome approach to causal inference
3. A taxonomy of assignment mechanisms
Part II. Classical Randomized Experiments:
4. A taxonomy of classical randomized experiments
5. Fisher's exact P-values for completely randomized experiments
6. Neyman's repeated sampling approach to completely randomized experiments
7. Regression methods for completely randomized experiments
8. Model-based inference in completely randomized experiments
9. Stratified randomized experiments
10. Paired randomized experiments
11. Case study: an experimental evaluation of a labor-market program
Part III. Regular Assignment Mechanisms: Design:
12. Unconfounded treatment assignment
13. Estimating the propensity score
14. Assessing overlap in covariate distributions
15. Design in observational studies: matching to ensure balance in covariate distributions
16. Design in observational studies: trimming to ensure balance in covariate distributions
Part IV. Regular Assignment Mechanisms: Analysis:
17. Subclassification on the propensity score
18. Matching estimators (Card-Krueger data)
19. Estimating the variance of estimators under unconfoundedness
20. Alternative estimands
Part V. Regular Assignment Mechanisms: Supplementary Analyses:
21. Assessing the unconfoundedness assumption
22. Sensitivity analysis and bounds
Part VI. Regular Assignment Mechanisms with Noncompliance: Analysis:
23. Instrumental-variables analysis of randomized experiments with one-sided noncompliance
24. Instrumental-variables analysis of randomized experiments with two-sided noncompliance
25. Model-based analyses with instrumental variables
Part VII. Conclusion:
26. Conclusions and extensions.
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1 有用 hzbbb 2019-11-08 23:10:54
Causal inference beyond Regressions. But still based on the Potential Outcome Framework.
1 有用 微、漠 2016-02-20 12:32:55
这本书让我觉得我之前统计学的东西都白学了。
0 有用 西瓜头 2020-05-22 09:25:44
read Part I, need some hands-on practice
6 有用 ◇ 2020-02-11 13:34:03
基本弃了,Rubin 体系的一家言,还这么长,还这么难懂。有其他评论说“Rubin有一种把简单事情将复杂的超能力”我看是对的。我看到过好几篇在 Rubin 体系工作的论文都是一脸懵逼,怕是被原始文献带坏了吧
1 有用 咖啡豆 2020-05-12 12:51:12
最近Imbens, Heckman, Pearl轮流翻
0 有用 近拙 2023-08-31 20:33:14 北京
择要览过,非常好。
0 有用 我只能选择天空 2022-11-19 02:43:58 四川
Imbens和Rubin两位大神在因果推断领域的大作,基于潜在结果理论和反事实框架展开,基本上很多经济金融/生物学方面的统计应用都有赖于这一成果,值得推荐
0 有用 风井 2022-07-04 17:30:07
everyday unobserved factor.
0 有用 白菜爱好者 2022-06-09 01:07:29
read to get ur hands dirty in CI
0 有用 ▽○▽ 2021-04-29 21:36:19
基于潜在结果(potential outcome)框架。比较冗长,有一些小错误。